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Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.Publicly available sequencing data can be used as external common controls for rare variant analyses but cautious sample ascertainment and processing is needed to avoid bias and confounding. The authors review opportunities and challenges for the robust use of common controls in genetic studies, including study design, infrastructure and quality control considerations.

Abstract Study Objectives Periodic limb movement in sleep is a common sleep phenotype characterized by repetitive leg movements that occur during or before sleep. We conducted a genome-wide association study (GWAS) of periodic limb movements in sleep (PLMS) using a joint analysis (i.e., discovery, replication, and joint meta-analysis) of four cohorts (MrOS, the Wisconsin Sleep Cohort Study, HypnoLaus, and MESA), comprised of 6843 total subjects. Methods The MrOS study and Wisconsin Sleep Cohort Study (N = 1745 cases) were used for discovery. Replication in the HypnoLaus and MESA cohorts (1002 cases) preceded joint meta-analysis. We also performed LD score regression, estimated heritability, and computed genetic correlations between potentially associated traits such as restless leg syndrome (RLS) and insomnia. The causality and direction of the relationships between PLMS and RLS was evaluated using Mendelian randomization. Results We found 2 independent loci were significantly associated with PLMS: rs113851554 (p = 3.51 × 10−12, β = 0.486), an SNP located in a putative regulatory element of intron eight of MEIS1 (2p14); and rs9369062 (p = 3.06 × 10−22, β = 0.2093), a SNP located in the intron region of BTBD9 (6p12); both of which were also lead signals in RLS GWAS. PLMS is genetically correlated with insomnia, risk of stroke, and RLS, but not with iron deficiency. Pleiotropy adjusted Mendelian randomization analysis identified a causal effect of RLS on PLMS. Conclusions Because PLMS is more common than RLS, PLMS may have multiple causes and additional studies are needed to further validate these findings.

Purpose Despite advances in cancer diagnosis and clinical care, survival for many primary brain and other central nervous system (CNS) tumors remain poor. This study performs a comprehensive survival analysis on these tumors. Methods Survival differences were determined utilizing the National Program of Cancer Registries Survival Analytic file for primary brain and CNS tumors. Overall survival and survival of the 5 most common histopathologies, within specific age groups, were determined. Overall survival was compared for three time periods: 2004–2007, 2008–2012, and 2013–2017. Survival differences were evaluated using Kaplan–Meier and multivariable Cox proportional hazards models. Models were adjusted for sex, race/ethnicity, and treatment. Malignant and non-malignant brain tumors were assessed separately. Results Among malignant brain and CNS tumor patients overall, there were notable differences in survival by time period among all age groups. Similar differences were noted in non-malignant brain and CNS tumor patients, except for adults (aged 40–64 years), where no survival changes were observed. Survival differences varied within specific histopathologies across age groups. There were improvements in survival in 2008–2012 and 2013–2017, when compared to 2004–2007, in children, AYA, and older adults with malignant tumors, and among older adults with non-malignant tumors. Conclusion Overall survival for malignant brain and other CNS tumors improved slightly in 2013–2017 for all age groups as compared to 2004–2007. Significant changes were observed for non-malignant brain and other CNS tumors among older adults. Information regarding survival over time can be utilized to identify population level effects of diagnostic and treatment improvements.

The complex polygenic nature of lung cancer is not fully characterized. Our study seeks to identify novel phenotypes associated with lung cancer using cross-trait linkage disequilibrium score regression (LDSR). We measured pairwise genetic correlation (r g ) and SNP heritability (h 2 ) between 347 traits and lung cancer risk using genome-wide association study summary statistics from the UKBB and OncoArray consortium. Further, we conducted analysis after removing genomic regions previously associated with smoking behaviors to mitigate potential confounding effects. We found significant negative genetic correlations between lung cancer risk and dietary behaviors, fitness metrics, educational attainment, and other psychosocial traits. Alcohol taken with meals (r g  = − 0.41, h 2  = 0.10, p = 1.33 × 10 –16 ), increased fluid intelligence scores (r g  = − 0.25, h 2  = 0.22, p = 4.54 × 10 –8 ), and the age at which full time education was completed (r g  = − 0.45, h 2  = 0.11, p = 1.24 × 10 –20 ) demonstrated negative genetic correlation with lung cancer susceptibility. The body mass index was positively correlated with lung cancer risk (r g  = 0.20, h 2  = 0.25, p = 2.61 × 10 –9 ). This analysis reveals shared genetic architecture between several traits and lung cancer predisposition. Future work should test for causal relationships and investigate common underlying genetic mechanisms across these genetically correlated traits.

Abstract Motivation Currently, the Polygenic Score (PGS) Catalog curates over 400 publications on over 500 traits corresponding to over 3000 polygenic risk scores (PRSs). To assess the feasibility of privately calculating the underlying multivariate relative risk for individuals with consumer genomics data, we developed an in-browserPRS calculator for genomic data that does not circulate any data or engage in any computation outside of the user's personal device. Results A prototype personal risk score calculator, created for research purposes, was developed to demonstrate how the PGS Catalog can be privately and readily applied to readily available direct-to-consumer genetic testing services, such as 23andMe. No software download, installation, or configuration is needed. The PRS web calculator matches individual PGS catalog entries with an individual's 23andMe genome data composed of 600k to 1.4 M single-nucleotide polymorphisms (SNPs). Beta coefficients provide researchers with a convenient assessment of risk associated with matched SNPs. This in-browser application was tested in a variety of personal devices, including smartphones, establishing the feasibility of privately calculating personal risk scores with up to a few thousand reference genetic variations and from the full 23andMe SNP data file (compressed or not). Availability and implementation The PRScalc web application is developed in JavaScript, HTML, and CSS and is available at GitHub repository (https://episphere.github.io/prs) under an MIT license. The datasets were derived from sources in the public domain: [PGS Catalog, Personal Genome Project].

To efficiently use limited resources, the brain must align its coding strategies with the statistical structure of input signals. A valuable test case for this is motion estimation in the fly visual system. Motion estimation is well-studied in flies, where motion estimates are encoded by activity in a population of wide-field motion sensitive neurons, the lobula plate tangential cells (LPTC). These neurons adapt to changes in stimulus statistics and their responses can be decoded to give extremely precise estimates of velocity. However, it is still not well-understood how the statistical structure of dynamic natural scenery affects the form and function of these cells' motion encodings. To better understand this, we began by first directly characterizing the joint distribution of fly-like visual inputs and motion trajectories in natural scenery using a specially designed fly-eye mimicking camera. The camera replicates the high temporal resolution, optical properties, and spectral sensitivity of the fly eye; attached inertial motion sensors provide ground truth about its rotations and translations through the world. Building on this, we demonstrate that the H1 neuron, a biological motion estimator in the fly visual system, is sensitive to changes in the distribution of the signal-to-noise ratio of visual inputs, which we control by altering the contrast and light intensity of visual stimuli. Finally, we tie these results together by demonstrating that H1's single-spike input-output behavior is strongly correlated with a mean-square error minimizing wide-field motion estimator, suggesting that H1 is highly tuned to the statistics of natural scenes. Our results can be summarized in two main points. First, the statistics of natural scenes, along with the optics of the fly visual system, are enough to generate motion estimation behavior similar to biological systems in optimal velocity estimators. Second, the fly's visual motion estimation system is both highly adaptive and well-tuned to the statistics of natural scenes, tending toward optimal behavior. Taken together, we believe these results imply that the statistics of natural scenes almost certainly help shape the computational properties of biological motion estimators and likely serve as one of the universal constraints that visual systems have evolved to handle.

Identifying features that interact to trigger disease, while accounting for heterogeneity across diverse populations, is essential for the development of precision and targeted medicine. Despite the availability of vast and complex health-related datasets, most existing works focus on identifying disease-associated features at the population level or within a few subpopulations, often overlooking individual-level heterogeneity within these groups. To address this limitation, we propose a novel framework that utilizes localized test statistics to identify disease-associated features tailored to individual profiles. Our method leverages the recently developed knockoffs methodology to control the noise level of the selection set so that the results are replicable. Moreover, it allows for the discovery of hidden heterogeneous effects within the data, as demonstrated in an application to single-cell RNA sequencing data for Alzheimer's disease. By aggregating localized feature selection results, our framework also enables powerful population-level feature selection. Our framework provides a powerful tool for exploratory studies of precision medicine, offering the potential to generate novel hypotheses for confirmatory biological experiments.

The AGN Space Telescope and Optical Reverberation Mapping 2 (STORM 2) campaign targeted Mrk 817 with intensive multi-wavelength monitoring and found its soft X-ray emission to be strongly absorbed. We present results from 157 near-IR spectra with an average cadence of a few days. Whereas the hot dust reverberation signal as tracked by the continuum flux does not have a clear response, we recover a dust reverberation radius of \\( 90\\) light-days from the blackbody dust temperature light-curve. This radius is consistent with previous photometric reverberation mapping results when Mrk 817 was in an unobscured state. The heating/cooling process we observe indicates that the inner limit of the dusty torus is set by a process other than sublimation, rendering it a luminosity-invariant `dusty wall' of a carbonaceous composition. Assuming thermal equilibrium for dust optically thick to the incident radiation, we derive a luminosity of \\( 6 10^44\\) erg s\\(^-1\\) for the source heating it. This luminosity is similar to that of the obscured spectral energy distribution, assuming a disk with an Eddington accretion rate of \\(m 0.2\\). Alternatively, the dust is illuminated by an unobscured lower luminosity disk with \\(m 0.1\\), which permits the UV/optical continuum lags in the high-obscuration state to be dominated by diffuse emission from the broad-line region. Finally, we find hot dust extended on scales \\(> 140-350\\) pc, associated with the rotating disk of ionised gas we observe in spatially-resolved [SIII] \\( 9531\\) images. Its likely origin is in the compact bulge of the barred spiral host galaxy, where it is heated by a nuclear starburst.

We present ground-based, multi-band light curves of the AGN Mrk~509, NGC\\,4151, and NGC\\,4593 obtained contemporaneously with \\sw\\, monitoring. We measure cross-correlation lags relative to \\sw\\, UVW2 (1928~\\AA) and test the standard prediction for disk reprocessing, which assumes a geometrically thin, optically thick accretion disk where continuum interband delays follow the relation \\( \\tau(\\lambda) \\propto \\lambda^{4/3} \\). For Mrk~509 the 273-d \\sw\\, campaign gives well-defined lags that increase with wavelength as \\(\\tau(\\lambda)\\propto\\lambda^{2.17\\pm0.2}\\), steeper than the thin-disk prediction, and the optical lags are a factor of \\(\\sim5\\) longer than expected for a simple disk-reprocessing model. This ``disk-size discrepancy'' as well as excess lags in the \\(u\\) and \\(r\\) bands (which include the Balmer continuum and H\\(\\alpha\\), respectively) suggest a mix of short lags from the disk and longer lags from nebular continuum originating in the broad-line region. The shorter \\sw\\, campaigns, 69~d on NGC\\,4151 and 22~d on NGC\\,4593, yield less well-defined, shorter lags \\(<2\\)~d. The NGC\\,4593 lags are consistent with \\(\\tau(\\lambda) \\propto \\lambda^{4/3}\\) but with uncertainties too large for a strong test. For NGC\\,4151 the \\sw\\, lags match \\(\\tau(\\lambda) \\propto \\lambda^{4/3}\\), with a small \\(U\\)-band excess, but the ground-based lags in the \\(r\\), \\(i\\), and \\(z\\) bands are significantly shorter than the \\(B\\) and \\(g\\) lags, and also shorter than expected from the thin-disk prediction. The interpretation of this unusual lag spectrum is unclear. Overall these results indicate significant diversity in the \\(\\tau-\\lambda\\) relation across the optical/UV/NIR, which differs from the more homogeneous behavior seen in the \\sw\\, bands.